MapMaker and PathTracer for tracking carbon in genome-scale metabolic models

Area of Science:

• Metabolic Engineering
• Systems Biology
• Computational Biology

Background:

• Interpreting large genome-scale metabolic models is challenging.
• Existing pathway analysis methods are not easily integrated with constraint-based modeling.

Purpose of the Study:

• Develop novel computational tools to address limitations in metabolic network analysis.
• Facilitate the identification of metabolite pathways within constraint-based models.

Main Methods:

• Developed MapMaker to predict carbon transfer maps (CTMs) using molecular formulae and stoichiometry.
• Developed PathTracer to find metabolite paths using CTMs as input.
• Applied MapMaker and PathTracer to the *Escherichia coli* iJO1366 model.

Main Results:

• MapMaker achieved >97% accuracy in assigning CTMs for reactions in the *E. coli* model.
• PathTracer successfully identified active and high-flux paths, including those for putrescine utilization and potential CO2 fixation.
• Demonstrated the utility of CTMs and COBRA constraints in path enumeration.

Conclusions:

• MapMaker and PathTracer enhance the interpretability of constraint-based models.
• These tools enable the identification of feasible, active, and high-flux metabolic pathways.
• The combined approach aids in understanding complex metabolic networks and biological processes.